apply multiple lambda functions with parameter in pandas

Question

I am finding the indexes of some values above certain cutoffs in a pandas DataFrame . So far I have achieved that using a series of lambda functions.

data.apply([lambda v:v[v>=0.25].idxmin(),
                                 lambda v:v[v>=0.25].idxmin(),
                                 lambda v:v[v>=0.50].idxmin(),
                                 lambda v:v[v>=0.75].idxmin(),
                                 lambda v:v[v>=0.90].idxmin()])

I have attempted to parametrize a lambda function to an arbitrary list of cutoff values. However, if I use the following, results are not correct as all lambda functions have the same name and basically only the last one is present in the dataframe returned by apply. How to parametrize these lambda correctly?

 cutoff_values=[25,50,100]
 agg_list=[lambda v,c:v[v>=(float(c)/100.0)].idxmin() for c in cutoff_values]
 data.apply(agg_list)

What would be a pythonic-pandasque better approach?

Is there a specific reason why you are using multiple lambdas and not a function? And maybe can you elaborate on my cutoff list is changing? — wolfstter
– wolfstter, Commented Dec 28, 2021 at 11:08
regarding my cutoff list is changing: I need to make my cutoff parameters — 00__00__00
– 00__00__00, Commented Dec 28, 2021 at 11:09
With a function it would be much easier to just hand over a set of cutoff values instead of copy and pasting the lambda functions. And with a named function you are not running in the problem of the fact that only the last one is executed. There you can do everything in one function. Or like @jezrael supposed in an answer use nested lambdas — wolfstter
– wolfstter, Commented Dec 28, 2021 at 11:16

jezrael · Accepted Answer · 2021-12-28 11:15:49Z

3

For me working nested lambda functions like:

q = lambda c: lambda x: x[x>=c].idxmin()
cutoff_values=[25,50,90]
print (data.apply([q((float(c)/100.0)) for c in cutoff_values]))

answered Dec 28, 2021 at 11:15

jezrael

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Muhammad Hassan · Accepted Answer · 2021-12-28 11:39:40Z

1

You can use this:

df = pd.DataFrame(data={'col':[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]})
df = df[['col']].apply(lambda x: [x[x >= (float(c) / 100.0)].idxmin() for c in cutoff_values])

answered Dec 28, 2021 at 11:39

Muhammad Hassan

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